Evidence map›Paper›PMID 40927289›Full record

ArticleFrontiers in endocrinology2025

Identification of lactylation-related biomarkers in osteoporosis from transcriptome and single-cell data.

Jiafeng Peng, Hongxing Zhang, Huaize Wang, Ting Jiang, Minglei Gao, Xingfu Ma, Yingzong Xiong, Yingchun Li, Ran Xu, Junchen Zhu

Abstract read
In one paragraph

Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Jiafeng Peng *Department of Orthopedics I, Second Affiliated Hospital, Anhui University of Traditional Chinese Medicine, Hefei, Anhui, China.
Hongxing Zhang *Department of Orthopedics I, Second Affiliated Hospital, Anhui University of Traditional Chinese Medicine, Hefei, Anhui, China.
Huaize Wang *Department of Orthopedics I, Second Affiliated Hospital, Anhui University of Traditional Chinese Medicine, Hefei, Anhui, China.
Ting JiangDepartment of Spinal Surgery, First Affiliated Hospital, Anhui University of Traditional Chinese Medicine, Hefei, China.
Minglei GaoDepartment of Orthopedics I, Second Affiliated Hospital, Anhui University of Traditional Chinese Medicine, Hefei, Anhui, China.
Xingfu MaDepartment of Orthopedics I, Second Affiliated Hospital, Anhui University of Traditional Chinese Medicine, Hefei, Anhui, China.
Yingzong XiongDepartment of Orthopedics I, Second Affiliated Hospital, Anhui University of Traditional Chinese Medicine, Hefei, Anhui, China.
Yingchun LiDepartment of Orthopedics I, Second Affiliated Hospital, Anhui University of Traditional Chinese Medicine, Hefei, Anhui, China.
Ran XuGraduate School, Anhui University of Traditional Chinese Medicine, Hefei, China.
Junchen ZhuDepartment of Orthopedics I, Second Affiliated Hospital, Anhui University of Traditional Chinese Medicine, Hefei, Anhui, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Emerging evidence indicates that lactase-mediated histone lactylation can activate osteogenic gene expression and promote bone formation. However, the role of lactylation-related genes (LRGs) in osteoporosis (OP) remains unclear. This study aims to clarify the key roles of LRGs and the molecular mechanisms of related biomarkers in OP. Methods: Three datasets (GSE7158, GSE56815, and GSE147287) and 327 LRGs were analyzed in this study. First, the biomarkers associated with OP were identified through differential gene expression analysis, machine learning algorithms, expression validation, and receiver operating characteristic (ROC) curve analysis. Subsequently, nomograms, functional enrichment analyses, immune infiltration analyses, regulatory network construction, drug prediction, and molecular docking were performed to characterize the functional and clinical significance of the biomarkers. Single-cell analysis was used to screen key cell types. Finally, reverse transcription quantitative polymerase chain reaction (RT-qPCR) was conducted to validate biomarker expression. Results: CSRP2 and FUBP1 can serve as biomarkers for the early prediction of osteoporosis risk in individuals with low peak bone mass or bone mineral density. The nomogram showed that these two biomarkers could accurately predict OP risk. Functional analysis revealed that Conclusions: CSRP2 and FUBP1 can serve as biomarkers for the early prediction of osteoporosis risk in individuals with low BMD/PBM. The findings of this study offer critical clinical guidance for OP prevention and treatment.

Indexed as

BiomarkersOsteoporosisTranscriptomeBone DensityDNA-Binding ProteinsFemaleHumansMaleRNA-Binding ProteinsSingle-Cell AnalysisBiomarkersDNA-Binding ProteinsRNA-Binding Proteinsbiomarkerslactylationnomogramosteoporosissingle-cell RNA sequencing

Identifiers

PMID40927289
PMCPMC12414738

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.